Overview

Dataset statistics

Number of variables20
Number of observations2793
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory522.8 KiB
Average record size in memory191.7 B

Variable types

Categorical9
Numeric11

Alerts

Route has a high cardinality: 2793 distinct valuesHigh cardinality
ORIGIN has a high cardinality: 262 distinct valuesHigh cardinality
DESTINATION has a high cardinality: 267 distinct valuesHigh cardinality
ORIGIN_CITY_NAME has a high cardinality: 256 distinct valuesHigh cardinality
DEST_CITY_NAME has a high cardinality: 262 distinct valuesHigh cardinality
Origin_NAME has a high cardinality: 262 distinct valuesHigh cardinality
Destination_NAME has a high cardinality: 267 distinct valuesHigh cardinality
DEP_DELAY is highly overall correlated with ARR_DELAY and 4 other fieldsHigh correlation
ARR_DELAY is highly overall correlated with DEP_DELAY and 4 other fieldsHigh correlation
AIR_TIME is highly overall correlated with DEP_DELAY and 4 other fieldsHigh correlation
Flight_Num is highly overall correlated with DEP_DELAY and 4 other fieldsHigh correlation
DISTANCE is highly overall correlated with DEP_DELAY and 4 other fieldsHigh correlation
Passenger_Num is highly overall correlated with DEP_DELAY and 4 other fieldsHigh correlation
Route is uniformly distributedUniform
Route has unique valuesUnique

Reproduction

Analysis started2023-09-22 19:02:28.270826
Analysis finished2023-09-22 19:02:42.834634
Duration14.56 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

Route
Categorical

HIGH CARDINALITY  UNIFORM  UNIQUE 

Distinct2793
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size43.6 KiB
ABE-ATL
 
1
ATL-EYW
 
1
GSO-IAD
 
1
GSP-IAD
 
1
HNL-IAD
 
1
Other values (2788)
2788 

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters19551
Distinct characters27
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2793 ?
Unique (%)100.0%

Sample

1st rowABE-ATL
2nd rowABQ-ATL
3rd rowABY-ATL
4th rowAEX-ATL
5th rowAGS-ATL

Common Values

ValueCountFrequency (%)
ABE-ATL 1
 
< 0.1%
ATL-EYW 1
 
< 0.1%
GSO-IAD 1
 
< 0.1%
GSP-IAD 1
 
< 0.1%
HNL-IAD 1
 
< 0.1%
HSV-IAD 1
 
< 0.1%
IAD-LWB 1
 
< 0.1%
IAD-MDT 1
 
< 0.1%
ALB-EWR 1
 
< 0.1%
EWR-EYW 1
 
< 0.1%
Other values (2783) 2783
99.6%

Length

2023-09-22T15:02:42.914596image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
abe-atl 1
 
< 0.1%
abe-clt 1
 
< 0.1%
dtw-fll 1
 
< 0.1%
alb-clt 1
 
< 0.1%
aby-atl 1
 
< 0.1%
aex-atl 1
 
< 0.1%
ags-atl 1
 
< 0.1%
alb-atl 1
 
< 0.1%
ase-atl 1
 
< 0.1%
atl-csg 1
 
< 0.1%
Other values (2783) 2783
99.6%

Most occurring characters

ValueCountFrequency (%)
- 2793
14.3%
A 1738
 
8.9%
S 1470
 
7.5%
L 1354
 
6.9%
D 1181
 
6.0%
C 884
 
4.5%
T 857
 
4.4%
M 851
 
4.4%
P 775
 
4.0%
O 766
 
3.9%
Other values (17) 6882
35.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 16758
85.7%
Dash Punctuation 2793
 
14.3%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 1738
 
10.4%
S 1470
 
8.8%
L 1354
 
8.1%
D 1181
 
7.0%
C 884
 
5.3%
T 857
 
5.1%
M 851
 
5.1%
P 775
 
4.6%
O 766
 
4.6%
I 719
 
4.3%
Other values (16) 6163
36.8%
Dash Punctuation
ValueCountFrequency (%)
- 2793
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 16758
85.7%
Common 2793
 
14.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 1738
 
10.4%
S 1470
 
8.8%
L 1354
 
8.1%
D 1181
 
7.0%
C 884
 
5.3%
T 857
 
5.1%
M 851
 
5.1%
P 775
 
4.6%
O 766
 
4.6%
I 719
 
4.3%
Other values (16) 6163
36.8%
Common
ValueCountFrequency (%)
- 2793
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 19551
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 2793
14.3%
A 1738
 
8.9%
S 1470
 
7.5%
L 1354
 
6.9%
D 1181
 
6.0%
C 884
 
4.5%
T 857
 
4.4%
M 851
 
4.4%
P 775
 
4.0%
O 766
 
3.9%
Other values (17) 6882
35.2%

ORIGIN
Categorical

Distinct262
Distinct (%)9.4%
Missing0
Missing (%)0.0%
Memory size43.6 KiB
ATL
 
145
DFW
 
122
DEN
 
114
CLT
 
105
DTW
 
72
Other values (257)
2235 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters8379
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique76 ?
Unique (%)2.7%

Sample

1st rowABE
2nd rowABQ
3rd rowABY
4th rowAEX
5th rowAGS

Common Values

ValueCountFrequency (%)
ATL 145
 
5.2%
DFW 122
 
4.4%
DEN 114
 
4.1%
CLT 105
 
3.8%
DTW 72
 
2.6%
DCA 65
 
2.3%
EWR 61
 
2.2%
IAH 59
 
2.1%
LAS 58
 
2.1%
BOS 57
 
2.0%
Other values (252) 1935
69.3%

Length

2023-09-22T15:02:43.002624image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
atl 145
 
5.2%
dfw 122
 
4.4%
den 114
 
4.1%
clt 105
 
3.8%
dtw 72
 
2.6%
dca 65
 
2.3%
ewr 61
 
2.2%
iah 59
 
2.1%
las 58
 
2.1%
bos 57
 
2.0%
Other values (252) 1935
69.3%

Most occurring characters

ValueCountFrequency (%)
A 1008
 
12.0%
L 797
 
9.5%
D 684
 
8.2%
C 519
 
6.2%
B 448
 
5.3%
S 433
 
5.2%
T 432
 
5.2%
I 377
 
4.5%
W 376
 
4.5%
E 368
 
4.4%
Other values (16) 2937
35.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 8379
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 1008
 
12.0%
L 797
 
9.5%
D 684
 
8.2%
C 519
 
6.2%
B 448
 
5.3%
S 433
 
5.2%
T 432
 
5.2%
I 377
 
4.5%
W 376
 
4.5%
E 368
 
4.4%
Other values (16) 2937
35.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 8379
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 1008
 
12.0%
L 797
 
9.5%
D 684
 
8.2%
C 519
 
6.2%
B 448
 
5.3%
S 433
 
5.2%
T 432
 
5.2%
I 377
 
4.5%
W 376
 
4.5%
E 368
 
4.4%
Other values (16) 2937
35.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8379
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 1008
 
12.0%
L 797
 
9.5%
D 684
 
8.2%
C 519
 
6.2%
B 448
 
5.3%
S 433
 
5.2%
T 432
 
5.2%
I 377
 
4.5%
W 376
 
4.5%
E 368
 
4.4%
Other values (16) 2937
35.1%

DESTINATION
Categorical

Distinct267
Distinct (%)9.6%
Missing0
Missing (%)0.0%
Memory size43.6 KiB
ORD
 
110
SLC
 
80
SEA
 
76
MSP
 
76
PHX
 
72
Other values (262)
2379 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters8379
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique80 ?
Unique (%)2.9%

Sample

1st rowATL
2nd rowATL
3rd rowATL
4th rowATL
5th rowATL

Common Values

ValueCountFrequency (%)
ORD 110
 
3.9%
SLC 80
 
2.9%
SEA 76
 
2.7%
MSP 76
 
2.7%
PHX 72
 
2.6%
SFO 71
 
2.5%
PHL 68
 
2.4%
TPA 62
 
2.2%
SFB 54
 
1.9%
STL 54
 
1.9%
Other values (257) 2070
74.1%

Length

2023-09-22T15:02:43.097596image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ord 110
 
3.9%
slc 80
 
2.9%
sea 76
 
2.7%
msp 76
 
2.7%
phx 72
 
2.6%
sfo 71
 
2.5%
phl 68
 
2.4%
tpa 62
 
2.2%
las 54
 
1.9%
stl 54
 
1.9%
Other values (257) 2070
74.1%

Most occurring characters

ValueCountFrequency (%)
S 1037
 
12.4%
A 730
 
8.7%
L 557
 
6.6%
P 555
 
6.6%
M 505
 
6.0%
D 497
 
5.9%
O 436
 
5.2%
T 425
 
5.1%
R 421
 
5.0%
F 369
 
4.4%
Other values (16) 2847
34.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 8379
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S 1037
 
12.4%
A 730
 
8.7%
L 557
 
6.6%
P 555
 
6.6%
M 505
 
6.0%
D 497
 
5.9%
O 436
 
5.2%
T 425
 
5.1%
R 421
 
5.0%
F 369
 
4.4%
Other values (16) 2847
34.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 8379
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
S 1037
 
12.4%
A 730
 
8.7%
L 557
 
6.6%
P 555
 
6.6%
M 505
 
6.0%
D 497
 
5.9%
O 436
 
5.2%
T 425
 
5.1%
R 421
 
5.0%
F 369
 
4.4%
Other values (16) 2847
34.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8379
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
S 1037
 
12.4%
A 730
 
8.7%
L 557
 
6.6%
P 555
 
6.6%
M 505
 
6.0%
D 497
 
5.9%
O 436
 
5.2%
T 425
 
5.1%
R 421
 
5.0%
F 369
 
4.4%
Other values (16) 2847
34.0%

ORIGIN_CITY_NAME
Categorical

Distinct256
Distinct (%)9.2%
Missing0
Missing (%)0.0%
Memory size43.6 KiB
Atlanta, GA
 
145
Dallas/Fort Worth, TX
 
122
Denver, CO
 
114
Washington, DC
 
107
Charlotte, NC
 
105
Other values (251)
2200 

Length

Max length30
Median length28
Mean length13.088077
Min length8

Characters and Unicode

Total characters36555
Distinct characters56
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique76 ?
Unique (%)2.7%

Sample

1st rowAllentown/Bethlehem/Easton, PA
2nd rowAlbuquerque, NM
3rd rowAlbany, GA
4th rowAlexandria, LA
5th rowAugusta, GA

Common Values

ValueCountFrequency (%)
Atlanta, GA 145
 
5.2%
Dallas/Fort Worth, TX 122
 
4.4%
Denver, CO 114
 
4.1%
Washington, DC 107
 
3.8%
Charlotte, NC 105
 
3.8%
Houston, TX 93
 
3.3%
Chicago, IL 79
 
2.8%
Detroit, MI 72
 
2.6%
New York, NY 67
 
2.4%
Phoenix, AZ 64
 
2.3%
Other values (246) 1825
65.3%

Length

2023-09-22T15:02:43.193597image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
tx 355
 
5.7%
fl 171
 
2.7%
ga 151
 
2.4%
co 150
 
2.4%
ca 147
 
2.3%
atlanta 145
 
2.3%
nc 140
 
2.2%
ny 133
 
2.1%
oh 127
 
2.0%
worth 122
 
1.9%
Other values (326) 4618
73.8%

Most occurring characters

ValueCountFrequency (%)
3466
 
9.5%
, 2793
 
7.6%
a 2619
 
7.2%
o 2052
 
5.6%
e 1897
 
5.2%
n 1875
 
5.1%
t 1846
 
5.0%
l 1787
 
4.9%
r 1469
 
4.0%
i 1423
 
3.9%
Other values (46) 15328
41.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 20746
56.8%
Uppercase Letter 9292
25.4%
Space Separator 3466
 
9.5%
Other Punctuation 3047
 
8.3%
Dash Punctuation 4
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 2619
12.6%
o 2052
9.9%
e 1897
9.1%
n 1875
9.0%
t 1846
8.9%
l 1787
8.6%
r 1469
7.1%
i 1423
 
6.9%
s 1313
 
6.3%
h 846
 
4.1%
Other values (16) 3619
17.4%
Uppercase Letter
ValueCountFrequency (%)
C 1065
 
11.5%
A 1021
 
11.0%
N 818
 
8.8%
D 630
 
6.8%
L 534
 
5.7%
M 525
 
5.7%
T 511
 
5.5%
O 447
 
4.8%
I 415
 
4.5%
F 412
 
4.4%
Other values (15) 2914
31.4%
Other Punctuation
ValueCountFrequency (%)
, 2793
91.7%
/ 236
 
7.7%
. 18
 
0.6%
Space Separator
ValueCountFrequency (%)
3466
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 30038
82.2%
Common 6517
 
17.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 2619
 
8.7%
o 2052
 
6.8%
e 1897
 
6.3%
n 1875
 
6.2%
t 1846
 
6.1%
l 1787
 
5.9%
r 1469
 
4.9%
i 1423
 
4.7%
s 1313
 
4.4%
C 1065
 
3.5%
Other values (41) 12692
42.3%
Common
ValueCountFrequency (%)
3466
53.2%
, 2793
42.9%
/ 236
 
3.6%
. 18
 
0.3%
- 4
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 36555
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3466
 
9.5%
, 2793
 
7.6%
a 2619
 
7.2%
o 2052
 
5.6%
e 1897
 
5.2%
n 1875
 
5.1%
t 1846
 
5.0%
l 1787
 
4.9%
r 1469
 
4.0%
i 1423
 
3.9%
Other values (46) 15328
41.9%

DEST_CITY_NAME
Categorical

Distinct262
Distinct (%)9.4%
Missing0
Missing (%)0.0%
Memory size43.6 KiB
Chicago, IL
 
142
Salt Lake City, UT
 
80
Minneapolis, MN
 
76
Seattle, WA
 
76
Phoenix, AZ
 
72
Other values (257)
2347 

Length

Max length34
Median length28
Mean length13.587898
Min length8

Characters and Unicode

Total characters37951
Distinct characters57
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique79 ?
Unique (%)2.8%

Sample

1st rowAtlanta, GA
2nd rowAtlanta, GA
3rd rowAtlanta, GA
4th rowAtlanta, GA
5th rowAtlanta, GA

Common Values

ValueCountFrequency (%)
Chicago, IL 142
 
5.1%
Salt Lake City, UT 80
 
2.9%
Minneapolis, MN 76
 
2.7%
Seattle, WA 76
 
2.7%
Phoenix, AZ 72
 
2.6%
San Francisco, CA 71
 
2.5%
Philadelphia, PA 68
 
2.4%
Houston, TX 65
 
2.3%
New York, NY 63
 
2.3%
Tampa, FL 62
 
2.2%
Other values (252) 2018
72.3%

Length

2023-09-22T15:02:43.305615image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
fl 406
 
6.1%
ca 347
 
5.2%
san 191
 
2.9%
tx 187
 
2.8%
il 164
 
2.5%
chicago 142
 
2.1%
city 142
 
2.1%
ny 118
 
1.8%
pa 114
 
1.7%
az 98
 
1.5%
Other values (329) 4745
71.3%

Most occurring characters

ValueCountFrequency (%)
3861
 
10.2%
a 2903
 
7.6%
, 2793
 
7.4%
e 2102
 
5.5%
o 1977
 
5.2%
n 1844
 
4.9%
i 1697
 
4.5%
t 1527
 
4.0%
l 1456
 
3.8%
s 1291
 
3.4%
Other values (47) 16500
43.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 21305
56.1%
Uppercase Letter 9674
25.5%
Space Separator 3861
 
10.2%
Other Punctuation 3110
 
8.2%
Dash Punctuation 1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 2903
13.6%
e 2102
9.9%
o 1977
9.3%
n 1844
8.7%
i 1697
 
8.0%
t 1527
 
7.2%
l 1456
 
6.8%
s 1291
 
6.1%
r 1288
 
6.0%
h 889
 
4.2%
Other values (16) 4331
20.3%
Uppercase Letter
ValueCountFrequency (%)
A 973
 
10.1%
L 969
 
10.0%
C 926
 
9.6%
S 764
 
7.9%
N 661
 
6.8%
F 635
 
6.6%
M 574
 
5.9%
P 509
 
5.3%
T 455
 
4.7%
O 413
 
4.3%
Other values (16) 2795
28.9%
Other Punctuation
ValueCountFrequency (%)
, 2793
89.8%
/ 223
 
7.2%
. 94
 
3.0%
Space Separator
ValueCountFrequency (%)
3861
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 30979
81.6%
Common 6972
 
18.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 2903
 
9.4%
e 2102
 
6.8%
o 1977
 
6.4%
n 1844
 
6.0%
i 1697
 
5.5%
t 1527
 
4.9%
l 1456
 
4.7%
s 1291
 
4.2%
r 1288
 
4.2%
A 973
 
3.1%
Other values (42) 13921
44.9%
Common
ValueCountFrequency (%)
3861
55.4%
, 2793
40.1%
/ 223
 
3.2%
. 94
 
1.3%
- 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 37951
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3861
 
10.2%
a 2903
 
7.6%
, 2793
 
7.4%
e 2102
 
5.5%
o 1977
 
5.2%
n 1844
 
4.9%
i 1697
 
4.5%
t 1527
 
4.0%
l 1456
 
3.8%
s 1291
 
3.4%
Other values (47) 16500
43.5%

DEP_DELAY
Real number (ℝ)

Distinct2526
Distinct (%)90.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7754.2786
Minimum0
Maximum164175
Zeros16
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size43.6 KiB
2023-09-22T15:02:43.437573image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile281
Q11862
median4627
Q39810
95-th percentile25982.8
Maximum164175
Range164175
Interquartile range (IQR)7948

Descriptive statistics

Standard deviation10249.228
Coefficient of variation (CV)1.3217513
Kurtosis41.326234
Mean7754.2786
Median Absolute Deviation (MAD)3415
Skewness4.5564081
Sum21657700
Variance1.0504667 × 108
MonotonicityNot monotonic
2023-09-22T15:02:43.556573image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 16
 
0.6%
3715 3
 
0.1%
3158 3
 
0.1%
60 3
 
0.1%
3340 3
 
0.1%
2475 3
 
0.1%
2377 3
 
0.1%
4476 3
 
0.1%
2509 3
 
0.1%
1581 3
 
0.1%
Other values (2516) 2750
98.5%
ValueCountFrequency (%)
0 16
0.6%
1 1
 
< 0.1%
2 1
 
< 0.1%
5 2
 
0.1%
6 1
 
< 0.1%
7 1
 
< 0.1%
10 1
 
< 0.1%
11 1
 
< 0.1%
12 1
 
< 0.1%
13 2
 
0.1%
ValueCountFrequency (%)
164175 1
< 0.1%
149123 1
< 0.1%
97935 1
< 0.1%
89563 1
< 0.1%
79597 1
< 0.1%
75429 1
< 0.1%
68654 1
< 0.1%
63883 1
< 0.1%
62952 1
< 0.1%
62522 1
< 0.1%

ARR_DELAY
Real number (ℝ)

Distinct2547
Distinct (%)91.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8387.2342
Minimum0
Maximum191244
Zeros18
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size43.6 KiB
2023-09-22T15:02:43.732928image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile294
Q11888
median5056
Q310406
95-th percentile27829.4
Maximum191244
Range191244
Interquartile range (IQR)8518

Descriptive statistics

Standard deviation11497.404
Coefficient of variation (CV)1.3708219
Kurtosis50.543514
Mean8387.2342
Median Absolute Deviation (MAD)3657
Skewness5.0417773
Sum23425545
Variance1.321903 × 108
MonotonicityNot monotonic
2023-09-22T15:02:43.859930image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 18
 
0.6%
1473 4
 
0.1%
621 4
 
0.1%
1428 4
 
0.1%
1188 3
 
0.1%
6232 3
 
0.1%
933 3
 
0.1%
4928 3
 
0.1%
1694 3
 
0.1%
1978 3
 
0.1%
Other values (2537) 2745
98.3%
ValueCountFrequency (%)
0 18
0.6%
1 1
 
< 0.1%
2 2
 
0.1%
3 2
 
0.1%
4 1
 
< 0.1%
7 1
 
< 0.1%
9 1
 
< 0.1%
11 1
 
< 0.1%
15 2
 
0.1%
17 1
 
< 0.1%
ValueCountFrequency (%)
191244 1
< 0.1%
181160 1
< 0.1%
108161 1
< 0.1%
105080 1
< 0.1%
88326 1
< 0.1%
85473 1
< 0.1%
75405 1
< 0.1%
71040 1
< 0.1%
69517 1
< 0.1%
69110 1
< 0.1%

AIR_TIME
Real number (ℝ)

Distinct2765
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean70891.195
Minimum2
Maximum1987018
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size43.6 KiB
2023-09-22T15:02:44.007929image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile3898
Q118532
median40993
Q383685
95-th percentile246018.6
Maximum1987018
Range1987016
Interquartile range (IQR)65153

Descriptive statistics

Standard deviation98773.078
Coefficient of variation (CV)1.3933053
Kurtosis66.607213
Mean70891.195
Median Absolute Deviation (MAD)27744
Skewness5.56066
Sum1.9799911 × 108
Variance9.756121 × 109
MonotonicityNot monotonic
2023-09-22T15:02:44.138927image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5670 2
 
0.1%
18258 2
 
0.1%
36607 2
 
0.1%
36968 2
 
0.1%
19350 2
 
0.1%
33774 2
 
0.1%
52475 2
 
0.1%
6634 2
 
0.1%
441 2
 
0.1%
43141 2
 
0.1%
Other values (2755) 2773
99.3%
ValueCountFrequency (%)
2 1
< 0.1%
59 1
< 0.1%
69 1
< 0.1%
90 1
< 0.1%
91 1
< 0.1%
107 1
< 0.1%
166 1
< 0.1%
180 1
< 0.1%
200 1
< 0.1%
214 1
< 0.1%
ValueCountFrequency (%)
1987018 1
< 0.1%
1213282 1
< 0.1%
847333 1
< 0.1%
823928 1
< 0.1%
787016 1
< 0.1%
763253 1
< 0.1%
755662 1
< 0.1%
752049 1
< 0.1%
748848 1
< 0.1%
669904 1
< 0.1%

Flight_Num
Real number (ℝ)

Distinct1250
Distinct (%)44.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean647.97888
Minimum1
Maximum8340
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size43.6 KiB
2023-09-22T15:02:44.266975image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile36
Q1176
median438
Q3839
95-th percentile2027
Maximum8340
Range8339
Interquartile range (IQR)663

Descriptive statistics

Standard deviation732.08773
Coefficient of variation (CV)1.1298019
Kurtosis14.09523
Mean647.97888
Median Absolute Deviation (MAD)285
Skewness2.8901386
Sum1809805
Variance535952.45
MonotonicityNot monotonic
2023-09-22T15:02:44.372973image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
180 42
 
1.5%
52 42
 
1.5%
50 33
 
1.2%
178 33
 
1.2%
176 24
 
0.9%
179 20
 
0.7%
174 19
 
0.7%
58 18
 
0.6%
177 16
 
0.6%
54 16
 
0.6%
Other values (1240) 2530
90.6%
ValueCountFrequency (%)
1 6
0.2%
2 12
0.4%
3 5
0.2%
4 10
0.4%
5 2
 
0.1%
6 3
 
0.1%
7 1
 
< 0.1%
8 12
0.4%
10 4
 
0.1%
12 2
 
0.1%
ValueCountFrequency (%)
8340 1
< 0.1%
7156 1
< 0.1%
6511 1
< 0.1%
6320 1
< 0.1%
4999 1
< 0.1%
4820 1
< 0.1%
4794 1
< 0.1%
4774 1
< 0.1%
4707 1
< 0.1%
4594 1
< 0.1%

DISTANCE
Real number (ℝ)

Distinct2761
Distinct (%)98.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean498458.11
Minimum237
Maximum15642000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size43.6 KiB
2023-09-22T15:02:44.492975image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum237
5-th percentile24561.6
Q1113142
median264710
Q3572136
95-th percentile1811911.2
Maximum15642000
Range15641763
Interquartile range (IQR)458994

Descriptive statistics

Standard deviation754231.75
Coefficient of variation (CV)1.5131296
Kurtosis76.866637
Mean498458.11
Median Absolute Deviation (MAD)187338
Skewness6.0680951
Sum1.3921935 × 109
Variance5.6886553 × 1011
MonotonicityNot monotonic
2023-09-22T15:02:44.606002image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
77372 3
 
0.1%
108936 3
 
0.1%
110160 3
 
0.1%
26520 2
 
0.1%
81000 2
 
0.1%
60860 2
 
0.1%
815915 2
 
0.1%
77520 2
 
0.1%
313950 2
 
0.1%
499800 2
 
0.1%
Other values (2751) 2770
99.2%
ValueCountFrequency (%)
237 1
< 0.1%
390 1
< 0.1%
448 1
< 0.1%
612 1
< 0.1%
655 1
< 0.1%
1135 1
< 0.1%
1183 1
< 0.1%
1254 1
< 0.1%
1339 1
< 0.1%
1358 1
< 0.1%
ValueCountFrequency (%)
15642000 1
< 0.1%
9622506 1
< 0.1%
6970212 1
< 0.1%
6481316 1
< 0.1%
6217560 1
< 0.1%
6100224 1
< 0.1%
6052298 1
< 0.1%
6009555 1
< 0.1%
5245348 1
< 0.1%
5187756 1
< 0.1%

Passenger_Num
Real number (ℝ)

Distinct2725
Distinct (%)97.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean84263.633
Minimum84
Maximum1082554
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size43.6 KiB
2023-09-22T15:02:44.731973image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum84
5-th percentile4542
Q122674
median56800
Q3109508
95-th percentile264124.8
Maximum1082554
Range1082470
Interquartile range (IQR)86834

Descriptive statistics

Standard deviation95163.68
Coefficient of variation (CV)1.1293565
Kurtosis14.058747
Mean84263.633
Median Absolute Deviation (MAD)37108
Skewness2.8874803
Sum2.3534833 × 108
Variance9.0561259 × 109
MonotonicityNot monotonic
2023-09-22T15:02:44.850025image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
23746 3
 
0.1%
7860 3
 
0.1%
83698 2
 
0.1%
84 2
 
0.1%
6458 2
 
0.1%
67942 2
 
0.1%
10202 2
 
0.1%
46736 2
 
0.1%
64842 2
 
0.1%
23220 2
 
0.1%
Other values (2715) 2771
99.2%
ValueCountFrequency (%)
84 2
0.1%
134 1
< 0.1%
142 1
< 0.1%
175.8281776 1
< 0.1%
187.6028402 1
< 0.1%
210 1
< 0.1%
214 1
< 0.1%
220 1
< 0.1%
224 1
< 0.1%
242 1
< 0.1%
ValueCountFrequency (%)
1082554 1
< 0.1%
929026 1
< 0.1%
845678 1
< 0.1%
821694 1
< 0.1%
654284 1
< 0.1%
627434 1
< 0.1%
620950 1
< 0.1%
620644 1
< 0.1%
611424 1
< 0.1%
598240 1
< 0.1%

ITIN_FARE
Real number (ℝ)

Distinct1426
Distinct (%)51.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean391.4539
Minimum0
Maximum1999
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size43.6 KiB
2023-09-22T15:02:44.970026image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile166.9
Q1312
median384.5
Q3468
95-th percentile604.5
Maximum1999
Range1999
Interquartile range (IQR)156

Descriptive statistics

Standard deviation135.1319
Coefficient of variation (CV)0.34520515
Kurtosis10.069061
Mean391.4539
Median Absolute Deviation (MAD)77.25
Skewness1.1836039
Sum1093330.8
Variance18260.631
MonotonicityNot monotonic
2023-09-22T15:02:45.083028image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
370.5 8
 
0.3%
466 7
 
0.3%
309 7
 
0.3%
355.5 7
 
0.3%
464.5 7
 
0.3%
444 7
 
0.3%
344 7
 
0.3%
333 7
 
0.3%
408 7
 
0.3%
310.5 7
 
0.3%
Other values (1416) 2722
97.5%
ValueCountFrequency (%)
0 1
< 0.1%
11 2
0.1%
43.5 1
< 0.1%
54.75 1
< 0.1%
66.5 1
< 0.1%
71.5 1
< 0.1%
72 1
< 0.1%
72.5 1
< 0.1%
75 1
< 0.1%
79.5 1
< 0.1%
ValueCountFrequency (%)
1999 1
< 0.1%
1465 1
< 0.1%
1307 1
< 0.1%
1172.5 1
< 0.1%
1069.5 1
< 0.1%
1055 1
< 0.1%
1017.5 1
< 0.1%
909 1
< 0.1%
902.75 1
< 0.1%
894.5 1
< 0.1%

Origin_TYPE
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size43.6 KiB
large_airport
2294 
medium_airport
499 

Length

Max length14
Median length13
Mean length13.178661
Min length13

Characters and Unicode

Total characters36808
Distinct characters13
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowmedium_airport
2nd rowlarge_airport
3rd rowmedium_airport
4th rowmedium_airport
5th rowlarge_airport

Common Values

ValueCountFrequency (%)
large_airport 2294
82.1%
medium_airport 499
 
17.9%

Length

2023-09-22T15:02:45.188061image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-22T15:02:45.283027image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
large_airport 2294
82.1%
medium_airport 499
 
17.9%

Most occurring characters

ValueCountFrequency (%)
r 7880
21.4%
a 5087
13.8%
i 3292
8.9%
e 2793
 
7.6%
_ 2793
 
7.6%
p 2793
 
7.6%
o 2793
 
7.6%
t 2793
 
7.6%
l 2294
 
6.2%
g 2294
 
6.2%
Other values (3) 1996
 
5.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 34015
92.4%
Connector Punctuation 2793
 
7.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 7880
23.2%
a 5087
15.0%
i 3292
9.7%
e 2793
 
8.2%
p 2793
 
8.2%
o 2793
 
8.2%
t 2793
 
8.2%
l 2294
 
6.7%
g 2294
 
6.7%
m 998
 
2.9%
Other values (2) 998
 
2.9%
Connector Punctuation
ValueCountFrequency (%)
_ 2793
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 34015
92.4%
Common 2793
 
7.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 7880
23.2%
a 5087
15.0%
i 3292
9.7%
e 2793
 
8.2%
p 2793
 
8.2%
o 2793
 
8.2%
t 2793
 
8.2%
l 2294
 
6.7%
g 2294
 
6.7%
m 998
 
2.9%
Other values (2) 998
 
2.9%
Common
ValueCountFrequency (%)
_ 2793
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 36808
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 7880
21.4%
a 5087
13.8%
i 3292
8.9%
e 2793
 
7.6%
_ 2793
 
7.6%
p 2793
 
7.6%
o 2793
 
7.6%
t 2793
 
7.6%
l 2294
 
6.2%
g 2294
 
6.2%
Other values (3) 1996
 
5.4%

Origin_NAME
Categorical

Distinct262
Distinct (%)9.4%
Missing0
Missing (%)0.0%
Memory size43.6 KiB
Hartsfield Jackson Atlanta International Airport
 
145
Dallas Fort Worth International Airport
 
122
Denver International Airport
 
114
Charlotte Douglas International Airport
 
105
Detroit Metropolitan Wayne County Airport
 
72
Other values (257)
2235 

Length

Max length60
Median length49
Mean length36.049767
Min length9

Characters and Unicode

Total characters100687
Distinct characters61
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique76 ?
Unique (%)2.7%

Sample

1st rowLehigh Valley International Airport
2nd rowAlbuquerque International Sunport
3rd rowSouthwest Georgia Regional Airport
4th rowAlexandria International Airport
5th rowAugusta Regional At Bush Field

Common Values

ValueCountFrequency (%)
Hartsfield Jackson Atlanta International Airport 145
 
5.2%
Dallas Fort Worth International Airport 122
 
4.4%
Denver International Airport 114
 
4.1%
Charlotte Douglas International Airport 105
 
3.8%
Detroit Metropolitan Wayne County Airport 72
 
2.6%
Ronald Reagan Washington National Airport 65
 
2.3%
Newark Liberty International Airport 61
 
2.2%
George Bush Intercontinental Houston Airport 59
 
2.1%
McCarran International Airport 58
 
2.1%
General Edward Lawrence Logan International Airport 57
 
2.0%
Other values (252) 1935
69.3%

Length

2023-09-22T15:02:45.388029image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
airport 2609
23.0%
international 1988
 
17.5%
fort 177
 
1.6%
field 170
 
1.5%
dallas 170
 
1.5%
jackson 152
 
1.3%
hartsfield 145
 
1.3%
atlanta 145
 
1.3%
worth 122
 
1.1%
regional 120
 
1.1%
Other values (434) 5542
48.9%

Most occurring characters

ValueCountFrequency (%)
r 10228
10.2%
n 9815
9.7%
t 9721
9.7%
a 8986
 
8.9%
8548
 
8.5%
o 8398
 
8.3%
i 7396
 
7.3%
e 6019
 
6.0%
l 5559
 
5.5%
A 3093
 
3.1%
Other values (51) 22924
22.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 79865
79.3%
Uppercase Letter 11805
 
11.7%
Space Separator 8548
 
8.5%
Other Punctuation 237
 
0.2%
Dash Punctuation 224
 
0.2%
Open Punctuation 3
 
< 0.1%
Close Punctuation 3
 
< 0.1%
Control 2
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 10228
12.8%
n 9815
12.3%
t 9721
12.2%
a 8986
11.3%
o 8398
10.5%
i 7396
9.3%
e 6019
7.5%
l 5559
7.0%
p 3009
 
3.8%
s 2017
 
2.5%
Other values (17) 8717
10.9%
Uppercase Letter
ValueCountFrequency (%)
A 3093
26.2%
I 2186
18.5%
C 694
 
5.9%
D 597
 
5.1%
W 506
 
4.3%
F 484
 
4.1%
M 482
 
4.1%
H 458
 
3.9%
L 444
 
3.8%
B 406
 
3.4%
Other values (14) 2455
20.8%
Other Punctuation
ValueCountFrequency (%)
/ 156
65.8%
' 49
 
20.7%
. 27
 
11.4%
& 5
 
2.1%
Control
ValueCountFrequency (%)
€ 1
50.0%
“ 1
50.0%
Space Separator
ValueCountFrequency (%)
8548
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 224
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 91670
91.0%
Common 9017
 
9.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 10228
11.2%
n 9815
10.7%
t 9721
10.6%
a 8986
9.8%
o 8398
9.2%
i 7396
 
8.1%
e 6019
 
6.6%
l 5559
 
6.1%
A 3093
 
3.4%
p 3009
 
3.3%
Other values (41) 19446
21.2%
Common
ValueCountFrequency (%)
8548
94.8%
- 224
 
2.5%
/ 156
 
1.7%
' 49
 
0.5%
. 27
 
0.3%
& 5
 
0.1%
( 3
 
< 0.1%
) 3
 
< 0.1%
€ 1
 
< 0.1%
“ 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 100684
> 99.9%
None 3
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 10228
10.2%
n 9815
9.7%
t 9721
9.7%
a 8986
 
8.9%
8548
 
8.5%
o 8398
 
8.3%
i 7396
 
7.3%
e 6019
 
6.0%
l 5559
 
5.5%
A 3093
 
3.1%
Other values (48) 22921
22.8%
None
ValueCountFrequency (%)
â 1
33.3%
€ 1
33.3%
“ 1
33.3%

Origin_Longitude
Real number (ℝ)

Distinct262
Distinct (%)9.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-93.167509
Minimum-176.646
Maximum-68.828102
Zeros0
Zeros (%)0.0%
Negative2793
Negative (%)100.0%
Memory size43.6 KiB
2023-09-22T15:02:45.517165image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-176.646
5-th percentile-122.309
Q1-104.673
median-86.753502
Q3-80.9431
95-th percentile-73.7789
Maximum-68.828102
Range107.81789
Interquartile range (IQR)23.729897

Descriptive statistics

Standard deviation17.819703
Coefficient of variation (CV)-0.1912652
Kurtosis2.3379739
Mean-93.167509
Median Absolute Deviation (MAD)9.7158049
Skewness-1.4076625
Sum-260216.85
Variance317.5418
MonotonicityNot monotonic
2023-09-22T15:02:45.645137image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-84.428101 145
 
5.2%
-97.038002 122
 
4.4%
-104.6729965 114
 
4.1%
-80.94309998 105
 
3.8%
-83.35340118 72
 
2.6%
-77.037697 65
 
2.3%
-74.16870117 61
 
2.2%
-95.34140015 59
 
2.1%
-115.1520004 58
 
2.1%
-71.00520325 57
 
2.0%
Other values (252) 1935
69.3%
ValueCountFrequency (%)
-176.6459961 1
 
< 0.1%
-162.598999 1
 
< 0.1%
-159.3390045 8
 
0.3%
-157.924228 23
0.8%
-156.951004 1
 
< 0.1%
-156.766008 2
 
0.1%
-156.429993 9
 
0.3%
-156.045603 11
0.4%
-155.0480042 2
 
0.1%
-152.4940033 1
 
< 0.1%
ValueCountFrequency (%)
-68.82810211 6
 
0.2%
-70.309303 2
 
0.1%
-70.82330322 1
 
< 0.1%
-71.00520325 57
2.0%
-71.420403 2
 
0.1%
-71.435699 2
 
0.1%
-71.8757019 1
 
< 0.1%
-72.68319702 24
0.9%
-72.88680267 1
 
< 0.1%
-73.10019684 5
 
0.2%

Origin_Latitude
Real number (ℝ)

Distinct262
Distinct (%)9.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.258775
Minimum19.721399
Maximum71.285402
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size43.6 KiB
2023-09-22T15:02:45.757165image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum19.721399
5-th percentile26.41312
Q133.434299
median38.805801
Q341.3032
95-th percentile45.7775
Maximum71.285402
Range51.564003
Interquartile range (IQR)7.8689005

Descriptive statistics

Standard deviation6.1144027
Coefficient of variation (CV)0.16410638
Kurtosis2.2513547
Mean37.258775
Median Absolute Deviation (MAD)3.765602
Skewness0.26340716
Sum104063.76
Variance37.38592
MonotonicityNot monotonic
2023-09-22T15:02:45.874138image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
33.6367 145
 
5.2%
32.896801 122
 
4.4%
39.86169815 114
 
4.1%
35.2140007 105
 
3.8%
42.21239853 72
 
2.6%
38.8521 65
 
2.3%
40.69250107 61
 
2.2%
29.9843998 59
 
2.1%
36.08010101 58
 
2.1%
42.36429977 57
 
2.0%
Other values (252) 1935
69.3%
ValueCountFrequency (%)
19.72139931 2
 
0.1%
19.738783 11
 
0.4%
20.78560066 1
 
< 0.1%
20.8986 9
 
0.3%
21.32062 23
0.8%
21.97599983 8
 
0.3%
24.55610085 4
 
0.1%
25.79319954 25
0.9%
25.90679932 4
 
0.1%
26.072599 50
1.8%
ValueCountFrequency (%)
71.285402 2
 
0.1%
66.88469696 1
 
< 0.1%
64.81510162 2
 
0.1%
61.17440033 17
0.6%
59.50329971 2
 
0.1%
58.35499954 4
 
0.1%
57.75 1
 
< 0.1%
56.80170059 1
 
< 0.1%
55.35559845 3
 
0.1%
51.87799835 1
 
< 0.1%

Destination_TYPE
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size43.6 KiB
large_airport
2371 
medium_airport
422 

Length

Max length14
Median length13
Mean length13.151092
Min length13

Characters and Unicode

Total characters36731
Distinct characters13
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowlarge_airport
2nd rowlarge_airport
3rd rowlarge_airport
4th rowlarge_airport
5th rowlarge_airport

Common Values

ValueCountFrequency (%)
large_airport 2371
84.9%
medium_airport 422
 
15.1%

Length

2023-09-22T15:02:45.979135image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-22T15:02:46.066136image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
large_airport 2371
84.9%
medium_airport 422
 
15.1%

Most occurring characters

ValueCountFrequency (%)
r 7957
21.7%
a 5164
14.1%
i 3215
8.8%
e 2793
 
7.6%
_ 2793
 
7.6%
p 2793
 
7.6%
o 2793
 
7.6%
t 2793
 
7.6%
l 2371
 
6.5%
g 2371
 
6.5%
Other values (3) 1688
 
4.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 33938
92.4%
Connector Punctuation 2793
 
7.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 7957
23.4%
a 5164
15.2%
i 3215
9.5%
e 2793
 
8.2%
p 2793
 
8.2%
o 2793
 
8.2%
t 2793
 
8.2%
l 2371
 
7.0%
g 2371
 
7.0%
m 844
 
2.5%
Other values (2) 844
 
2.5%
Connector Punctuation
ValueCountFrequency (%)
_ 2793
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 33938
92.4%
Common 2793
 
7.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 7957
23.4%
a 5164
15.2%
i 3215
9.5%
e 2793
 
8.2%
p 2793
 
8.2%
o 2793
 
8.2%
t 2793
 
8.2%
l 2371
 
7.0%
g 2371
 
7.0%
m 844
 
2.5%
Other values (2) 844
 
2.5%
Common
ValueCountFrequency (%)
_ 2793
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 36731
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 7957
21.7%
a 5164
14.1%
i 3215
8.8%
e 2793
 
7.6%
_ 2793
 
7.6%
p 2793
 
7.6%
o 2793
 
7.6%
t 2793
 
7.6%
l 2371
 
6.5%
g 2371
 
6.5%
Other values (3) 1688
 
4.6%

Destination_NAME
Categorical

Distinct267
Distinct (%)9.6%
Missing0
Missing (%)0.0%
Memory size43.6 KiB
Chicago O'Hare International Airport
 
110
Salt Lake City International Airport
 
80
Seattle Tacoma International Airport
 
76
Minneapolis-St Paul International/Wold-Chamberlain Airport
 
76
Phoenix Sky Harbor International Airport
 
72
Other values (262)
2379 

Length

Max length60
Median length48
Mean length34.883996
Min length12

Characters and Unicode

Total characters97431
Distinct characters60
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique80 ?
Unique (%)2.9%

Sample

1st rowHartsfield Jackson Atlanta International Airport
2nd rowHartsfield Jackson Atlanta International Airport
3rd rowHartsfield Jackson Atlanta International Airport
4th rowHartsfield Jackson Atlanta International Airport
5th rowHartsfield Jackson Atlanta International Airport

Common Values

ValueCountFrequency (%)
Chicago O'Hare International Airport 110
 
3.9%
Salt Lake City International Airport 80
 
2.9%
Seattle Tacoma International Airport 76
 
2.7%
Minneapolis-St Paul International/Wold-Chamberlain Airport 76
 
2.7%
Phoenix Sky Harbor International Airport 72
 
2.6%
San Francisco International Airport 71
 
2.5%
Philadelphia International Airport 68
 
2.4%
Tampa International Airport 62
 
2.2%
Orlando Sanford International Airport 54
 
1.9%
St Louis Lambert International Airport 54
 
1.9%
Other values (257) 2070
74.1%

Length

2023-09-22T15:02:46.163229image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
airport 2679
24.0%
international 2075
 
18.6%
san 191
 
1.7%
regional 172
 
1.5%
chicago 145
 
1.3%
city 120
 
1.1%
o'hare 110
 
1.0%
field 107
 
1.0%
orlando 101
 
0.9%
st 93
 
0.8%
Other values (456) 5349
48.0%

Most occurring characters

ValueCountFrequency (%)
r 10178
10.4%
n 9614
9.9%
t 9450
9.7%
a 8935
9.2%
8350
 
8.6%
o 8158
 
8.4%
i 7495
 
7.7%
e 5655
 
5.8%
l 4861
 
5.0%
p 3167
 
3.3%
Other values (50) 21568
22.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 76882
78.9%
Uppercase Letter 11651
 
12.0%
Space Separator 8350
 
8.6%
Other Punctuation 304
 
0.3%
Dash Punctuation 216
 
0.2%
Control 14
 
< 0.1%
Close Punctuation 7
 
< 0.1%
Open Punctuation 7
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 10178
13.2%
n 9614
12.5%
t 9450
12.3%
a 8935
11.6%
o 8158
10.6%
i 7495
9.7%
e 5655
7.4%
l 4861
6.3%
p 3167
 
4.1%
s 1528
 
2.0%
Other values (16) 7841
10.2%
Uppercase Letter
ValueCountFrequency (%)
A 2924
25.1%
I 2234
19.2%
S 937
 
8.0%
C 624
 
5.4%
M 520
 
4.5%
P 455
 
3.9%
L 430
 
3.7%
F 396
 
3.4%
H 383
 
3.3%
R 366
 
3.1%
Other values (14) 2382
20.4%
Other Punctuation
ValueCountFrequency (%)
/ 122
40.1%
' 110
36.2%
. 63
20.7%
& 9
 
3.0%
Control
ValueCountFrequency (%)
€ 7
50.0%
“ 7
50.0%
Space Separator
ValueCountFrequency (%)
8350
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 216
100.0%
Close Punctuation
ValueCountFrequency (%)
) 7
100.0%
Open Punctuation
ValueCountFrequency (%)
( 7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 88533
90.9%
Common 8898
 
9.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 10178
11.5%
n 9614
10.9%
t 9450
10.7%
a 8935
10.1%
o 8158
9.2%
i 7495
8.5%
e 5655
 
6.4%
l 4861
 
5.5%
p 3167
 
3.6%
A 2924
 
3.3%
Other values (40) 18096
20.4%
Common
ValueCountFrequency (%)
8350
93.8%
- 216
 
2.4%
/ 122
 
1.4%
' 110
 
1.2%
. 63
 
0.7%
& 9
 
0.1%
) 7
 
0.1%
( 7
 
0.1%
€ 7
 
0.1%
“ 7
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 97410
> 99.9%
None 21
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 10178
10.4%
n 9614
9.9%
t 9450
9.7%
a 8935
9.2%
8350
 
8.6%
o 8158
 
8.4%
i 7495
 
7.7%
e 5655
 
5.8%
l 4861
 
5.0%
p 3167
 
3.3%
Other values (47) 21547
22.1%
None
ValueCountFrequency (%)
€ 7
33.3%
“ 7
33.3%
â 7
33.3%

Destination_Longitude
Real number (ℝ)

Distinct267
Distinct (%)9.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-95.67852
Minimum-165.44501
Maximum-70.309303
Zeros0
Zeros (%)0.0%
Negative2793
Negative (%)100.0%
Memory size43.6 KiB
2023-09-22T15:02:46.276227image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-165.44501
5-th percentile-122.375
Q1-111.983
median-90.258003
Q3-81.308998
95-th percentile-74.168701
Maximum-70.309303
Range95.135704
Interquartile range (IQR)30.674004

Descriptive statistics

Standard deviation17.789109
Coefficient of variation (CV)-0.18592583
Kurtosis0.22314025
Mean-95.67852
Median Absolute Deviation (MAD)10.105301
Skewness-0.84544363
Sum-267230.11
Variance316.45239
MonotonicityNot monotonic
2023-09-22T15:02:46.416256image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-87.9048 110
 
3.9%
-111.9779968 80
 
2.9%
-122.308998 76
 
2.7%
-93.221802 76
 
2.7%
-112.012001 72
 
2.6%
-122.375 71
 
2.5%
-75.2410965 68
 
2.4%
-82.53320312 62
 
2.2%
-81.23750305 54
 
1.9%
-90.370003 54
 
1.9%
Other values (257) 2070
74.1%
ValueCountFrequency (%)
-165.4450073 2
 
0.1%
-162.598999 1
 
< 0.1%
-161.8379974 1
 
< 0.1%
-159.3390045 4
 
0.1%
-157.924228 6
0.2%
-157.095993 1
 
< 0.1%
-156.6730042 1
 
< 0.1%
-156.429993 10
0.4%
-156.045603 5
0.2%
-155.0480042 1
 
< 0.1%
ValueCountFrequency (%)
-70.309303 12
0.4%
-70.82330322 1
 
< 0.1%
-71.00520325 3
 
0.1%
-71.420403 14
0.5%
-71.435699 10
0.4%
-71.8757019 3
 
0.1%
-72.68319702 1
 
< 0.1%
-72.88680267 1
 
< 0.1%
-73.10019684 2
 
0.1%
-73.15329742 1
 
< 0.1%

Destination_Latitude
Real number (ℝ)

Distinct267
Distinct (%)9.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.845718
Minimum19.721399
Maximum70.194702
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size43.6 KiB
2023-09-22T15:02:46.847229image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum19.721399
5-th percentile26.5362
Q132.733601
median37.618999
Q340.788399
95-th percentile45.588699
Maximum70.194702
Range50.473303
Interquartile range (IQR)8.0547981

Descriptive statistics

Standard deviation6.1957032
Coefficient of variation (CV)0.1681526
Kurtosis1.1029963
Mean36.845718
Median Absolute Deviation (MAD)4.1847
Skewness0.17973533
Sum102910.09
Variance38.386738
MonotonicityNot monotonic
2023-09-22T15:02:47.020738image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
41.9786 110
 
3.9%
40.78839874 80
 
2.9%
47.449001 76
 
2.7%
44.882 76
 
2.7%
33.43429947 72
 
2.6%
37.61899948 71
 
2.5%
39.87189865 68
 
2.4%
27.97550011 62
 
2.2%
28.77759933 54
 
1.9%
38.748697 54
 
1.9%
Other values (257) 2070
74.1%
ValueCountFrequency (%)
19.72139931 1
 
< 0.1%
19.738783 5
 
0.2%
20.8986 10
 
0.4%
20.96290016 1
 
< 0.1%
21.1529007 1
 
< 0.1%
21.32062 6
 
0.2%
21.97599983 4
 
0.1%
24.55610085 5
 
0.2%
25.79319954 36
1.3%
26.072599 23
0.8%
ValueCountFrequency (%)
70.19470215 2
0.1%
66.88469696 1
 
< 0.1%
64.81510162 2
0.1%
64.5121994 2
0.1%
61.17440033 3
0.1%
60.77980042 1
 
< 0.1%
60.4917984 2
0.1%
58.35499954 2
0.1%
57.04710007 2
0.1%
56.80170059 1
 
< 0.1%

Interactions

2023-09-22T15:02:41.006203image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-22T15:02:29.045295image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-22T15:02:30.296798image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-22T15:02:31.482275image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-22T15:02:32.635426image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-22T15:02:33.837597image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-22T15:02:35.023791image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-22T15:02:36.198573image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-22T15:02:37.529016image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-22T15:02:38.700005image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-22T15:02:39.878318image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-22T15:02:41.103204image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-22T15:02:29.180297image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-22T15:02:30.404270image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-22T15:02:31.584274image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-22T15:02:32.826452image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-22T15:02:33.950598image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-22T15:02:35.123818image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-22T15:02:36.313611image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-22T15:02:37.644015image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-22T15:02:38.811090image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-22T15:02:39.981325image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-22T15:02:41.226203image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-22T15:02:29.288295image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-22T15:02:30.518299image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-22T15:02:31.695301image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-22T15:02:32.932423image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-22T15:02:34.064598image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-22T15:02:35.236901image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-22T15:02:36.421578image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-22T15:02:37.763265image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-22T15:02:38.941089image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-22T15:02:40.089354image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-22T15:02:41.343614image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-22T15:02:29.401295image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-22T15:02:30.627272image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-22T15:02:31.804271image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-22T15:02:33.036424image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-22T15:02:34.179599image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-22T15:02:35.355871image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-22T15:02:36.544665image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-22T15:02:37.872395image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-22T15:02:39.047130image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-22T15:02:40.194356image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-22T15:02:41.453618image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-22T15:02:29.495295image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-22T15:02:30.724273image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-22T15:02:31.902276image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-22T15:02:33.123422image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-22T15:02:34.281680image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-22T15:02:35.482395image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-22T15:02:36.649709image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-22T15:02:37.968927image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-22T15:02:39.148126image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-22T15:02:40.290354image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-22T15:02:41.574615image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-22T15:02:29.694679image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-22T15:02:30.834272image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-22T15:02:32.015426image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-22T15:02:33.223425image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-22T15:02:34.397677image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-22T15:02:35.602396image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-22T15:02:36.884708image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-22T15:02:38.093011image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-22T15:02:39.260230image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-22T15:02:40.412889image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-22T15:02:41.687616image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-22T15:02:29.792681image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-22T15:02:30.943270image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-22T15:02:32.117423image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-22T15:02:33.317423image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-22T15:02:34.502679image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-22T15:02:35.702498image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-22T15:02:36.989708image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-22T15:02:38.198002image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-22T15:02:39.359239image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-22T15:02:40.517714image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-22T15:02:41.961615image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-22T15:02:29.897679image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-22T15:02:31.052274image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-22T15:02:32.227423image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-22T15:02:33.426517image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-22T15:02:34.611681image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-22T15:02:35.804498image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-22T15:02:37.106750image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-22T15:02:38.302005image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-22T15:02:39.474237image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-22T15:02:40.621713image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-22T15:02:42.075634image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-22T15:02:29.998675image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-22T15:02:31.159274image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-22T15:02:32.329451image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-22T15:02:33.533518image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-22T15:02:34.718787image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-22T15:02:35.898494image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-22T15:02:37.212747image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-22T15:02:38.407003image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-22T15:02:39.577309image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-22T15:02:40.718206image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-22T15:02:42.178632image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-22T15:02:30.098799image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-22T15:02:31.264271image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-22T15:02:32.432424image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-22T15:02:33.635553image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-22T15:02:34.821821image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-22T15:02:35.999496image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-22T15:02:37.314750image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-22T15:02:38.512007image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-22T15:02:39.672318image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-22T15:02:40.814204image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-22T15:02:42.281631image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-22T15:02:30.198797image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-22T15:02:31.371300image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-22T15:02:32.532422image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-22T15:02:33.738596image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-22T15:02:34.923788image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-22T15:02:36.099578image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-22T15:02:37.425991image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-22T15:02:38.605005image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-22T15:02:39.768319image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-22T15:02:40.913202image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2023-09-22T15:02:47.152735image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
DEP_DELAYARR_DELAYAIR_TIMEFlight_NumDISTANCEPassenger_NumITIN_FAREOrigin_LongitudeOrigin_LatitudeDestination_LongitudeDestination_LatitudeOrigin_TYPEDestination_TYPE
DEP_DELAY1.0000.9910.8170.9380.7590.9380.1430.0830.0320.0570.0840.1070.136
ARR_DELAY0.9911.0000.8040.9330.7430.9330.1600.0820.0420.0590.0980.1000.126
AIR_TIME0.8170.8041.0000.8550.9870.8540.2430.048-0.067-0.083-0.0070.1170.106
Flight_Num0.9380.9330.8551.0000.7921.0000.1570.050-0.0280.0380.0350.1780.158
DISTANCE0.7590.7430.9870.7921.0000.7910.2340.041-0.072-0.103-0.0140.1060.096
Passenger_Num0.9380.9330.8541.0000.7911.0000.1560.050-0.0280.0390.0350.1770.157
ITIN_FARE0.1430.1600.2430.1570.2340.1561.0000.057-0.018-0.0680.1000.1820.207
Origin_Longitude0.0830.0820.0480.0500.0410.0500.0571.0000.1290.483-0.0860.3720.158
Origin_Latitude0.0320.042-0.067-0.028-0.072-0.028-0.0180.1291.000-0.0060.0770.3580.160
Destination_Longitude0.0570.059-0.0830.038-0.1030.039-0.0680.483-0.0061.000-0.0790.1470.310
Destination_Latitude0.0840.098-0.0070.035-0.0140.0350.100-0.0860.077-0.0791.0000.1520.281
Origin_TYPE0.1070.1000.1170.1780.1060.1770.1820.3720.3580.1470.1521.0000.059
Destination_TYPE0.1360.1260.1060.1580.0960.1570.2070.1580.1600.3100.2810.0591.000

Missing values

2023-09-22T15:02:42.455632image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-09-22T15:02:42.711631image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

RouteORIGINDESTINATIONORIGIN_CITY_NAMEDEST_CITY_NAMEDEP_DELAYARR_DELAYAIR_TIMEFlight_NumDISTANCEPassenger_NumITIN_FAREOrigin_TYPEOrigin_NAMEOrigin_LongitudeOrigin_LatitudeDestination_TYPEDestination_NAMEDestination_LongitudeDestination_Latitude
0ABE-ATLABEATLAllentown/Bethlehem/Easton, PAAtlanta, GA3637.004933.0043270.00434300328.0056796.00573.00medium_airportLehigh Valley International Airport-75.4440.65large_airportHartsfield Jackson Atlanta International Airport-84.4333.64
1ABQ-ATLABQATLAlbuquerque, NMAtlanta, GA1664.002016.0054067.00330418770.0043214.00458.25large_airportAlbuquerque International Sunport-106.6135.04large_airportHartsfield Jackson Atlanta International Airport-84.4333.64
2ABY-ATLABYATLAlbany, GAAtlanta, GA5036.004814.0016953.0049872210.0064616.00462.75medium_airportSouthwest Georgia Regional Airport-84.1931.54large_airportHartsfield Jackson Atlanta International Airport-84.4333.64
3AEX-ATLAEXATLAlexandria, LAAtlanta, GA5010.004730.0049247.00640320000.0082778.00505.50medium_airportAlexandria International Airport-92.5531.33large_airportHartsfield Jackson Atlanta International Airport-84.4333.64
4AGS-ATLAGSATLAugusta, GAAtlanta, GA12268.0012636.0043867.001367195481.00176880.00404.00large_airportAugusta Regional At Bush Field-81.9633.37large_airportHartsfield Jackson Atlanta International Airport-84.4333.64
5ALB-ATLALBATLAlbany, NYAtlanta, GA2800.002660.0060980.00500426500.0064904.00513.25medium_airportAlbany International Airport-73.8042.75large_airportHartsfield Jackson Atlanta International Airport-84.4333.64
6ASE-ATLASEATLAspen, COAtlanta, GA2819.002669.0030315.00173225592.0022038.00669.25medium_airportAspen-Pitkin Co/Sardy Field-106.8739.22large_airportHartsfield Jackson Atlanta International Airport-84.4333.64
7ATL-CSGCSGATLColumbus, GAAtlanta, GA3412.003602.007185.0027722991.0036652.00474.00medium_airportColumbus Metropolitan Airport-84.9432.52large_airportHartsfield Jackson Atlanta International Airport-84.4333.64
8ABE-CLTABECLTAllentown/Bethlehem/Easton, PACharlotte, NC3588.004470.0042947.00502241462.0066374.00440.00medium_airportLehigh Valley International Airport-75.4440.65large_airportCharlotte Douglas International Airport-80.9435.21
9AGS-CLTAGSCLTAugusta, GACharlotte, NC11626.0011443.0031339.00966135240.00126368.00358.50large_airportAugusta Regional At Bush Field-81.9633.37large_airportCharlotte Douglas International Airport-80.9435.21
RouteORIGINDESTINATIONORIGIN_CITY_NAMEDEST_CITY_NAMEDEP_DELAYARR_DELAYAIR_TIMEFlight_NumDISTANCEPassenger_NumITIN_FAREOrigin_TYPEOrigin_NAMEOrigin_LongitudeOrigin_LatitudeDestination_TYPEDestination_NAMEDestination_LongitudeDestination_Latitude
2783PSG-WRGPSGWRGPetersburg, AKWrangell, AK2298.002431.002225.001735363.0022446.00340.25medium_airportPetersburg James A Johnson Airport-132.9556.80medium_airportWrangell Airport-132.3756.48
2784LAS-SMXLASSMXLas Vegas, NVSanta Maria, CA863.001020.004946.009027900.0012044.00116.75large_airportMcCarran International Airport-115.1536.08medium_airportSanta Maria Pub/Capt G Allan Hancock Field-120.4634.90
2785LAX-RDDLAXRDDLos Angeles, CARedding, CA488.00381.003725.004924549.006012.00266.00large_airportLos Angeles International Airport-118.4133.94medium_airportRedding Municipal Airport-122.2940.51
2786MCO-PSMMCOPSMOrlando, FLPortsmouth, NH268.00308.0012133.007587525.0010134.0091.75large_airportOrlando International Airport-81.3128.43medium_airportPortsmouth International at Pease Airport-70.8243.08
2787ORD-PAHORDPAHChicago, ILPaducah, KY4579.004844.0019290.00299102258.0039474.00321.25large_airportChicago O'Hare International Airport-87.9041.98medium_airportBarkley Regional Airport-88.7737.06
2788ORD-SLNORDSLNChicago, ILSalina, KS4573.005184.0012742.0014681614.0018266.00247.75large_airportChicago O'Hare International Airport-87.9041.98medium_airportSalina Municipal Airport-97.6538.79
2789ORD-UINORDUINChicago, ILQuincy, IL5106.005314.0011679.0025757054.0032860.00168.00large_airportChicago O'Hare International Airport-87.9041.98medium_airportQuincy Regional Baldwin Field-91.1939.94
2790MSP-RHIMSPRHIMinneapolis, MNRhinelander, WI5093.006720.0013429.0034465360.0044470.00252.50large_airportMinneapolis-St Paul International/Wold-Chamberlain Airport-93.2244.88medium_airportRhinelander Oneida County Airport-89.4745.63
2791SEA-YKMSEAYKMSeattle, WAYakima, WA6456.007864.0017512.0061062830.0079180.00232.00large_airportSeattle Tacoma International Airport-122.3147.45medium_airportYakima Air Terminal McAllister Field-120.5446.57
2792SLC-TWFSLCTWFSalt Lake City, UTTwin Falls, ID4428.004641.0019656.00582101850.0077760.00292.75large_airportSalt Lake City International Airport-111.9840.79medium_airportJoslin Field Magic Valley Regional Airport-114.4942.48